## AIphabet: The A-to-Z of Artificial Intelligence

**“AIphabet: Decoding AI, Letter by Letter”** 🤖

From algorithms to neural nets, we unravel the mystery, A journey through AI’s lexicon, where knowledge sets us free. Whether you’re a data wizard or just curious to explore, Let’s dive into the alphabet of AI—there’s so much more!

## A

**Abductive Logic Programming**: A form of logic programming that deals with abductive reasoning, which involves finding the simplest and most likely explanation for observations.**Abductive Reasoning**: A method of logical inference which starts with an observation then seeks to find the simplest and most likely explanation.**Abstract Data Type**: A mathematical model for data types, where a data type is defined by its behavior from the point of view of a user of the data.**Abstraction**: The process of removing physical, spatial, or temporal details to focus on the essential characteristics of a program or computational process.**Accelerating Change**: The hypothesis that technological change is exponential, as each new technology and advancement builds upon the previous, leading to faster and more significant changes.**Accuracy**: In AI, the degree to which the result of a computation, algorithm, or measurement conforms to the correct value or a standard.**Actionable Intelligence**: Information that can be acted upon, with the implication that actions should be taken, and the information is immediately relevant to the actions.**Active Learning**: A special case of machine learning in which a learning algorithm can query a user interactively to label data with the desired outputs.**Adaptive System**: A system that changes its behavior based on feedback from the environment.**Adversarial Machine Learning**: A technique used in machine learning which attempts to fool models through malicious input.**Agent**: In AI, an entity that perceives its environment and takes actions that maximize its chances of achieving its goals.**Agglomerative Clustering**: A type of hierarchical clustering that builds nested clusters by merging or splitting them successively.**Algorithm**: A set of rules or steps used to solve a problem or perform a computation.**Algorithmic Bias**: Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.**AlphaGo**: An AI program developed by Google DeepMind to play the board game Go, known for defeating world champion Lee Sedol.**Ambient Intelligence**: Electronic environments that are sensitive and responsive to the presence of people.**Analogical Reasoning**: The process of finding a commonality between two things and inferring a new concept.**Analytics**: The discovery, interpretation, and communication of meaningful patterns in data.**ANN (Artificial Neural Network)**: A computational model based on the structure and functions of biological neural networks.**Anomaly Detection**: The identification of items, events, or observations which do not conform to an expected pattern or other items in a dataset.**Ant Colony Optimization**: A probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs**Algorithm**: An algorithm is a sequence of rules given to an AI machine to perform a task or solve a problem. Common algorithms include classification, regression, and clustering.**Application Programming Interface (API)**: An API, or application programming interface, is a set of protocols that determine how two software applications will interact with each other. APIs tend to be written in programming languages such as C++ or JavaScript.**Artificial General Intelligence (AGI)**: AGI refers to a type of artificial intelligence that has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.**Artificial Narrow Intelligence (ANI)**: ANI, also known as weak AI, refers to AI systems designed to handle one particular task or a set of related tasks. It lacks the general cognitive abilities of AGI.**Artificial Neural Network (ANN)**: A computational model based on the structure and functions of biological neural networks. ANNs are used for tasks such as image recognition, natural language processing, and regression.**Autonomous Surface Vessels (ASVs)**: Robotic vehicles that operate on the sea surface, primarily used for recording oceanographic data. They utilize different propulsion methods, including wave-powered or propeller-driven systems. ASVs are significant for their ability to conduct marine research without requiring a crew on board.

## B

**Backpropagation**: A method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data is processed.**Bayesian Network**: A statistical model that represents a set of variables and their conditional dependencies via a directed acyclic graph.**Behavioral Cloning**: A method by which human subcognitive skills can be transferred to an AI system.**Benchmark**: In AI, a standard test set used to evaluate the performance of an algorithm or system.**Bias**: The tendency of a machine learning model to consistently learn the wrong thing by not taking into account all aspects of the data.**Big Data**: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.**Binary Classification**: A type of classification task that outputs one of two mutually exclusive classes.**Bioinformatics**: The science of collecting and analyzing complex biological data such as genetic codes.**Biometrics**: The measurement and statistical analysis of people’s unique physical and behavioral characteristics.**Bipedal Robot**: A robot that has two legs and walks in a manner similar to a human.**Bit**: The basic unit of information in computing and digital communications.**Black Box Model**: A system where the inputs and outputs are known, but the internal workings are not.**Blockchain**: A system in which a record of transactions made in bitcoin or another cryptocurrency is maintained across several computers that are linked in a peer-to-peer network.**Boltzmann Machine**: A type of stochastic recurrent neural network.**Boosting**: A machine learning ensemble meta-algorithm for primarily reducing bias and variance.**Bot**: A software application that runs automated tasks over the internet.**Bounding Box**: In computer vision, a box drawn around objects of interest in an image.**Brain-Computer Interface**: A direct communication pathway between an enhanced or wired brain and an external device.**Branch and Bound**: An algorithm design paradigm for discrete and combinatorial optimization problems.**Breast Cancer Detection**: Using AI to analyze mammograms for signs of breast cancer.**Broad AI**: AI with a wide range of cognitive abilities, similar to general human intelligence.**Brute Force Algorithm**: A straightforward approach to solving a problem that relies on sheer computing power and trying every possibility rather than advanced techniques to improve efficiency.**Buffer**: A region of memory used to temporarily hold data while it is being moved from one place to another.**Bug**: An error, flaw, failure, or fault in a computer program or system that causes it to produce an incorrect or unexpected result.**Byte**: A group of binary digits or bits (usually eight) operated on as a unit.**Byzantine Fault Tolerance**: A property of distributed computing systems that allows them to reach consensus even when some of the nodes fail to respond or respond with incorrect information.**BYOAI:**Bring Your Own AI, is a practice where employees use their personal AI tools and models for work-related tasks.

## C

**Caffe**: An open-source deep learning framework1.**Capsule Networks**: An alternative to CNNs using capsules to represent parts of objects1.**Chatbot**: A software application designed to simulate human conversation1.**ChatGPT**: A conversational AI model by OpenAI1.**Classification**: The process of predicting the class or category of a given input.**Clustering**: The task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.**Cognitive Computing**: A subset of AI that attempts to mimic human thought processes.**Collaborative Filtering**: A method used by recommender systems to make automatic predictions about the interests of a user by collecting preferences from many users.**Computer Vision**: A field of AI that trains computers to interpret and understand the visual world.**Convolutional Neural Network (CNN)**: A deep learning algorithm which can take in an input image, assign importance to various aspects/objects in the image, and be able to differentiate one from the other.**Cost Function**: A function that the AI system attempts to minimize during training.**Cross-Validation**: A technique for assessing how the results of a statistical analysis will generalize to an independent data set.**CUDA**: A parallel computing platform and application programming interface model created by Nvidia.**Curriculum Learning**: A type of learning in which the AI system is gradually exposed to more complex concepts.**Custom Vision**: A service within Microsoft’s Cognitive Services that lets you build, deploy, and improve your own image classifiers.**Cybernetics**: The interdisciplinary study of the structure of regulatory systems.**Cyc**: A long-living AI project that aims to assemble a comprehensive ontology and knowledge base of everyday common sense knowledge.**Continuous Learning**: The ability of an AI system to continually acquire, fine-tune, and transfer knowledge and skills throughout its lifespan2.**Conversational AI**: A branch of AI that focuses on enabling computers to engage in natural and human-like conversations2.**Convolutional Neural Networks (CNNs)**: A specialized type of neural network designed to process and analyze visual data2.**Contextual Bandits**: A type of reinforcement learning algorithm where the agent chooses actions based on the current context.**Control Theory**: A subfield of mathematics that deals with the control of continuously operating dynamical systems.**Convergence**: The process where a machine learning algorithm makes better predictions over time.**Convex Optimization**: A subfield of optimization that studies the problem of minimizing convex functions over convex sets.**Corpus**: A large collection of texts used for studying language.**Creativity**: The use of imagination or original ideas to create something; in AI, it refers to the ability of a system to generate new, novel, and valuable ideas or artifacts.**Credit Assignment Path (CAP)**: The process of understanding the contribution of each part of a neural network to the final output.**Crowdsourcing**: The practice of obtaining input or information from a large number of people via the Internet.**Curated Data**: Data that has been selected, organized, and presented using professional or expert knowledge.**Cyborg**: A being with both organic and biomechatronic body parts, often referred to in discussions about the future of AI and enhancement technologies

## D

**Data Augmentation**: The process of increasing the diversity of data available for training models without actually collecting new data1.**Data Bias**: Systematic and unfair inaccuracies in data that can lead to skewed outcomes in AI models2.**Data Labeling/Annotation**: The process of identifying raw data (like images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it2.**Data Leakage**: Occurs when information from outside the training dataset is used to create the model, leading to overfitting1.**Data Mining**: The practice of examining large pre-existing databases to generate new information1.**Data Science**: An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.**Data Visualization**: The graphic representation of data to find patterns, trends, and correlations that might go undetected in text-based data.**Deep Learning**: A subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled.**Deep Neural Network**: An artificial neural network with multiple layers between the input and output layers.**Decision Tree**: A decision support tool that uses a tree-like graph or model of decisions and their possible consequences.**Deductive Reasoning**: The process of reasoning from one or more statements to reach a logically certain conclusion.**Dense Layer**: A fully connected neural network layer where each input node is connected to each output node.**Deployment**: The process of integrating a machine learning model into an existing production environment to make practical use of its predictions.**Dimensionality Reduction**: The process of reducing the number of random variables under consideration by obtaining a set of principal variables.**Discriminative Model**: A type of model in machine learning that models the dependence of an unobserved variable ( y ) on an observed variable ( x ).**Distributed AI**: AI systems that are distributed across different machines, which can communicate and coordinate actions to achieve a goal.**Docker**: An open platform for developing, shipping, and running applications, often used to deploy AI applications.**Domain Knowledge**: Expertise in a specific, specialized discipline or field, necessary to ensure that an AI system functions correctly within its context.**Dropout**: A regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data.**Dynamic Programming**: A method for solving complex problems by breaking them down into simpler subproblems.**Dynamic System**: A system characterized by constant change, activity, or progress, particularly relevant in AI for systems that adapt and learn from new data.**DQN (Deep Q-Network)**: Combines Q-Learning with deep neural networks to let RL agents learn how to act optimally in controlled, grid-like environments.**DSL (Domain-Specific Language)**: A computer language specialized to a particular application domain.**Dueling Networks**: A type of neural network architecture for reinforcement learning that separately estimates (duels) the value of the state and the advantages of each action.**Data Wrangling**: The process of cleaning, structuring, and enriching raw data into a desired format for better decision making in less time.**Decision Analysis**: The process of making decisions based on research and systematic modeling of tradeoffs.**Decision Support System**: A computer-based information system that supports business or organizational decision-making activities.**Deconvolutional Networks**: A type of neural network that is used in deep learning to perform operations such as image segmentation and object detection.**Deep Belief Network**: A generative graphical model composed of multiple layers of latent variables with connections between the layers but not between units within each layer.**Deep Reinforcement Learning**: Combining deep learning with reinforcement learning, where the artificial agent learns to make decisions by executing actions and observing the results

## E

**Eager Learning**: A learning paradigm where a model is trained on the entire dataset at once.**Edge Computing**: Processing data at the edge of the network, near the source of the data.**Eigenvalue**: A value that represents the magnitude of a vector in a transformation represented by a matrix.**Eliza Effect**: The tendency to attribute human-like understanding to computer behaviors.**Embedding Layer**: A layer in neural networks that transforms categorical data into numerical format.**Emotion Recognition**: AI techniques used to detect and interpret human emotions.**Encoder**: A neural network component that compresses input into a smaller, dense representation.**Ensemble Learning**: Combining multiple models to improve predictive performance.**Entity Extraction**: Identifying and classifying named entities in text into predefined categories.**Entropy**: A measure of randomness or uncertainty in a system.**Episodic Memory**: The ability of an AI to recall specific events or experiences.**Epoch**: One complete pass through the entire training dataset during the learning process.**Error Backpropagation**: A method used to calculate gradients for training neural networks.**Estimator**: An algorithm that makes predictions based on data.**Ethics in AI**: The study of moral issues and standards related to artificial intelligence.**Euclidean Distance**: The straight-line distance between two points in Euclidean space.**Euler’s Method**: A numerical technique for solving ordinary differential equations.**Evolutionary Algorithm**: Algorithms inspired by biological evolution to solve optimization problems.**ExaFLOP**: A unit of computing performance equal to one quintillion floating-point operations per second.**Expert System**: A computer system that emulates the decision-making ability of a human expert.**Exploratory Data Analysis**: An approach to analyzing data sets to summarize their main characteristics.**Exponential Smoothing**: A time series forecasting method for univariate data.**Extended Reality (XR)**: An umbrella term encompassing augmented reality (AR), virtual reality (VR), and mixed reality (MR).**Extraction Layer**: A part of a neural network that extracts features from raw data.**Extreme Learning Machine**: A learning algorithm for single-layer feedforward neural networks.**Extrinsic Motivation**: Motivation driven by external rewards, as opposed to intrinsic motivation, which is driven by personal satisfaction

## F

**Feature Extraction**: The process of transforming raw data into numerical features that can be processed by AI algorithms.**Feature Selection**: The technique of selecting a subset of relevant features for use in model construction.**Feedforward Neural Network**: A type of neural network where connections between the nodes do not form a cycle.**Federated Learning**: A machine learning approach where the model is trained across multiple decentralized devices or servers.**FIFO (First In, First Out)**: An ordering method where the first element added to a queue will be the first one to be removed.**Fine-Tuning**: The process of adjusting the parameters of an already trained model to improve its performance or adapt it to a new task.**Fitness Function**: A function used in genetic algorithms to evaluate how close a given design solution is to achieving the set aims.**FLAIR**: A state-of-the-art natural language processing library for training custom models.**FLOPS (Floating Point Operations Per Second)**: A measure of computer performance, especially in fields of scientific calculations that make heavy use of floating-point calculations.**Focal Loss**: A loss function used to address class imbalance during training of machine learning models.**Fog Computing**: An architecture that uses edge devices to carry out a substantial amount of computation, storage, and communication locally and routed over the internet backbone.**Forward Chaining**: A method of reasoning in AI that starts with known facts and applies inference rules to extract more data until a goal is reached.**Fourier Transform**: A mathematical transform that decomposes functions depending on space or time into functions depending on spatial or temporal frequency.**FP-Growth (Frequent Pattern Growth)**: An algorithm used for finding frequent item sets in a dataset for use in association rule learning.**Frame Problem**: The challenge of specifying what does not change when an action is taken in AI and robotics.**Fuzzy Logic**: A form of many-valued logic that deals with approximate, rather than fixed and exact reasoning.**Fuzzy Set**: A set without a crisp, clearly defined boundary. It can contain elements with only a partial degree of membership.**Fully Connected Layer**: A layer in a neural network where each neuron is connected to every neuron in the previous layer.**Function Approximation**: The process of estimating a function that approximates a target function in supervised learning tasks.**Functional Programming**: A programming paradigm where programs are constructed by applying and composing functions.**Future State Maximization**: In reinforcement learning, it’s the strategy of choosing actions based on the maximization of expected future rewards.**Federated Transfer Learning**: Combining federated learning and transfer learning to improve model performance with decentralized data.**Feature Engineering**: The process of using domain knowledge to extract features from raw data that make machine learning algorithms work.**Feature Map**: The output of one filter applied to the previous layer, which may be an image or another feature map.**Feedback Loop**: A system structure that allows for output to be fed back as input, often leading to changes in the system.**Feedforward Control**: A control strategy that adjusts system behaviors based on anticipated changes without waiting for feedback.**Field Programmable Gate Array (FPGA)**: An integrated circuit designed to be configured by a customer or a designer after manufacturing.**Finite State Machine**: A computational model used to design computer programs and sequential logic circuits.**Fisher’s Linear Discriminant**: A method used in statistics, pattern recognition, and machine learning to find a linear combination of features that separates two or more classes of objects or events.**Fixed Policy**: In reinforcement learning, a policy that does not change over time or in response to the environment.

## G

**Gabor Filter**: A linear filter used in image processing for edge detection.**Gated Recurrent Unit (GRU)**: A type of recurrent neural network that is effective at capturing dependencies in sequences.**Gaussian Distribution**: A probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence.**Gaussian Mixture Model (GMM)**: A probabilistic model for representing normally distributed subpopulations within an overall population.**Gaussian Noise**: A statistical noise having a probability density function equal to that of the normal distribution, which is also known as Gaussian distribution.**Gaussian Process**: A collection of random variables, any finite number of which have a joint Gaussian distribution.**General Adversarial Network (GAN)**: A class of machine learning frameworks designed by Ian Goodfellow and his colleagues, used for generative modeling.**General AI**: Artificial intelligence that exhibits human-like intelligence and behaviors, capable of performing any intellectual task that a human being can.**Genetic Algorithm**: A search heuristic that mimics the process of natural selection to generate useful solutions to optimization and search problems.**Genetic Programming**: An evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.**Geometric Deep Learning**: A field of study that generalizes neural network models to non-Euclidean domains such as graphs and manifolds.**Gesture Recognition**: The mathematical interpretation of a human motion by a computing device.**Gibbs Sampling**: A Markov chain Monte Carlo (MCMC) algorithm for obtaining a sequence of observations which are approximated from a specified multivariate probability distribution.**Gini Impurity**: A measure of the likelihood of an incorrect classification of a new instance if it were randomly classified according to the distribution of class labels from the dataset.**Global Optimization**: The process of finding the best solution from all feasible solutions.**Gradient Boosting**: A machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models.**Gradient Descent**: An optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient.**Graph Convolutional Network (GCN)**: A type of neural network that operates directly on graphs and can take advantage of their structural information.**Graph Neural Network (GNN)**: A type of neural network that directly operates on the graph structure, allowing it to take into account the relationships between nodes.**Greedy Algorithm**: An algorithmic paradigm that follows the problem-solving heuristic of making the locally optimal choice at each stage.**Grid Search**: An exhaustive searching through a manually specified subset of the hyperparameter space of a learning algorithm.**Grokking**: A sudden and profound understanding of something complex.**Ground Truth**: The accuracy of training set’s classification for supervised learning techniques.**Group Method of Data Handling (GMDH)**: A family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets.**Gumbel Distribution**: A probability distribution used for modeling the distribution of the maximum (or the minimum) number of samples of various distributions.**Gumbel-Softmax Distribution**: A continuous distribution over the simplex that can approximate samples from a categorical distribution.**Gym Environment**: A toolkit for developing and comparing reinforcement learning algorithms.**Gini Coefficient**: A measure of statistical dispersion intended to represent the income inequality or wealth inequality within a nation or a social group.**Graph Database**: A database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.**Graph Embedding**: The process of transforming nodes, edges, and their features into vector space while preserving graph topology and property information.

## H

**Hebbian Learning**: A theory that proposes an algorithm for changing synaptic weight based on the correlation of activity between pairs of neurons.**Heuristic**: A technique designed to solve a problem more quickly when classic methods are too slow, or to find an approximate solution when classic methods fail to find any exact solution.**Hidden Layer**: Layers of neurons in a neural network that are neither input nor output layers; they are part of the internal structure that processes inputs into outputs.**Hidden Markov Model (HMM)**: A statistical model in which the system being modeled is assumed to be a Markov process with unobservable states.**Hierarchical Clustering**: A method of cluster analysis which seeks to build a hierarchy of clusters.**Hierarchical Reinforcement Learning**: A method in reinforcement learning where hierarchies of agents operate at different levels of abstraction.**High-Dimensional Data**: Data with many features or dimensions, which can complicate analysis due to the curse of dimensionality.**Hill Climbing**: An optimization algorithm that starts with an arbitrary solution and iteratively makes small changes to the solution, each time improving it a little.**Hinge Loss**: A type of loss function used primarily for training classifiers, particularly support vector machines.**Homomorphic Encryption**: A form of encryption that allows computation on ciphertexts, generating an encrypted result which, when decrypted, matches the result of operations performed on the plaintext.**Hopfield Network**: A form of recurrent artificial neural network that serves as content-addressable memory systems with binary threshold nodes.**Hyperparameter**: A parameter whose value is set before the learning process begins.**Hyperparameter Optimization**: The process of finding the set of hyperparameters for a learning algorithm that yields the best performance.**Hypothesis Testing**: A method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis.**Hadamard Product**: An element-wise product of two matrices of the same dimension, resulting in a new matrix of the same dimension.**Hamming Distance**: A metric for comparing two binary data strings. It measures the minimum number of substitutions required to change one string into the other.**Hardware Acceleration**: The use of computer hardware to perform some functions more efficiently than is possible in software running on a more general-purpose CPU.**Hash Function**: A function that can be used to map data of arbitrary size to fixed-size values.**Heuristic Search**: A search method for finding a solution to a problem by incrementally building and evaluating the solutions based on a set of rules or a heuristic.**Heterogeneous Computing**: A type of computing architecture where systems use more than one kind of processor or cores.**Heteroscedasticity**: A property of a set of random variables where the variability of the random disturbance is different across elements of the set.**Hierarchical Task Network (HTN)**: A method for decomposing complex AI planning problems into smaller, more manageable sub-tasks.**Hilbert Space**: A concept from functional analysis that generalizes the notion of Euclidean space. It extends methods of vector algebra from the two-dimensional plane and three-dimensional space to spaces with any finite or infinite number of dimensions.**Hinge Function**: A piecewise-linear function often used in machine learning, particularly in support vector machines.**Histogram Equalization**: A method in image processing of contrast adjustment using the image’s histogram.**Homoscedasticity**: The assumption in a statistical model that the variance within each group being compared is the same across all groups and levels of independent variables.**Huber Loss**: A loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss.**Human-in-the-Loop (HITL)**: A model of interaction where a human is involved in the loop of a machine learning process, providing feedback and decisions.**Human-Level AI**: AI that has the capacity to understand, learn, and perform tasks at a level of competence comparable to that of a human.**Hybrid Model**: A model that combines two or more different approaches to modeling, often a combination of machine learning models with rule-based systems.

## I

**Image Classification**: The process of categorizing and labeling groups of pixels or vectors within an image based on specific rules.**Image Recognition**: The ability of AI to detect and identify objects or features within a digital image.**Image Segmentation**: The process of partitioning a digital image into multiple segments to simplify or change the representation of an image into something more meaningful.**Imbalanced Dataset**: A dataset in which the classes are not represented equally.**Imitation Learning**: A technique where models learn to perform tasks by mimicking the actions of experts.**Impact Factor**: A measure reflecting the yearly average number of citations to recent articles published in a journal.**Impulse Response**: The output of a system when presented with a brief input signal, called an impulse.**Inception Network**: A deep convolutional neural network architecture that was introduced and popularized by Google.**Incremental Learning**: A method of machine learning in which input data is continuously used to extend the existing model’s knowledge i.e., to further train the model.**Inductive Bias**: The set of assumptions that a learning algorithm uses to predict outputs given inputs that it has not encountered.**Inductive Logic Programming (ILP)**: A subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge, and hypotheses.**Inductive Reasoning**: A logical process in which multiple premises, all believed true or found true most of the time, are combined to obtain a specific conclusion.**Inference Engine**: The component of an expert system that applies logical rules to the knowledge base to deduce new information.**Information Gain**: A measure used in decision trees that quantifies the reduction in entropy or surprise from transforming a dataset in some way.**Information Retrieval**: The activity of obtaining information system resources that are relevant to an information need from a collection of those resources.**Information Theory**: A branch of applied mathematics and electrical engineering involving the quantification of information.**Informed Search**: A search algorithm that uses problem-specific knowledge to find solutions more efficiently than an uninformed search algorithm.**Inheritance**: In object-oriented programming, a mechanism where new classes can be derived from existing classes.**Instance-Based Learning**: A family of learning algorithms that compare new problem instances with instances seen in training, which have been stored in memory.**Instruction Set Architecture (ISA)**: The part of the computer architecture related to programming, including the native data types, instructions, registers, etc.**Integrated Development Environment (IDE)**: A software application that provides comprehensive facilities to computer programmers for software development.**Intelligent Agent**: An autonomous entity which observes and acts upon an environment and directs its activity towards achieving goals.**Intelligent Automation (IA)**: Assists humans in tasks, enhancing productivity and decision-making. IA tools are designed to augment our cognitive capabilities, making us smarter, faster, and more efficient.**Intelligent Tutoring System (ITS)**: A computer system that aims to provide immediate and customized instruction or feedback to learners, often in educational settings.**Interpolation**: A method of constructing new data points within the range of a discrete set of known data points.**Inverse Reinforcement Learning (IRL)**: A machine learning technique that infers the underlying reward function from observed behavior.**IoT (Internet of Things)**: A network of interconnected physical devices, vehicles, buildings, and other objects embedded with sensors, software, and network connectivity.**Iterative Deepening**: A search algorithm that combines the benefits of depth-first and breadth-first search by repeatedly applying depth-limited search with increasing depth limits.**Iterative Method**: A process that repeats a series of steps until a specific condition is met.**I-vector (Identity Vector)**: A low-dimensional representation of speaker characteristics used in speaker recognition systems.**Invariance**: The property of remaining unchanged under a specified transformation

## J

**Jaccard Index**: A statistic used for gauging the similarity and diversity of sample sets.**Jacobian Matrix**: A matrix of all first-order partial derivatives of a vector-valued function.**Java**: A high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible, commonly used in AI for its portability.**Jensen-Shannon Divergence**: A method of measuring the similarity between two probability distributions. It is based on the Kullback–Leibler divergence, with some modifications to ensure symmetry.**Joint Probability**: The probability of two events happening at the same time.**Joint Probability Distribution**: A probability distribution for a random vector, describing the probability of each possible outcome.**Jupyter Notebook**: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.**Just-In-Time Compilation (JIT)**: A runtime process that compiles code into machine language just before it is executed to improve performance.**JVM (Java Virtual Machine)**: An abstract computing machine that enables a computer to run a Java program.**JSON (JavaScript Object Notation)**: A lightweight data-interchange format that is easy for humans to read and write, and easy for machines to parse and generate.**Julia**: A high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.**Jump Connection**: In neural networks, a connection that skips one or more layers.**JupyterLab**: The next-generation web-based user interface for Project Jupyter, offering all the familiar building blocks of the classic Jupyter Notebook in a flexible and powerful user interface.**Just-Enough Learning (JEL)**: A concept in machine learning where the model learns just enough to perform the task required, avoiding overfitting.**Just-Noticeable Difference (JND)**: The minimum difference in stimulation that a person can detect 50% of the time.**JVM Languages**: Languages that are designed to run on the Java Virtual Machine, aside from Java itself, such as Scala, Kotlin, and Clojure.**JADE (Java Agent DEvelopment Framework)**: A software framework for the development of intelligent agents, in compliance with the FIPA specifications for interoperable intelligent multi-agent systems.**Jini**: A set of Java programs that can offer services to other Java programs in a networked environment.**JIT Compiler**: A Just-In-Time Compiler that translates bytecode into machine code at runtime for execution by the host CPU.**Job Scheduling**: In AI, the process of assigning resources to perform a set of tasks, often with the goal of optimizing overall performance or throughput.**Johnson-Lindenstrauss Lemma**: A mathematical result concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space.**Joint Action**: In multi-agent systems, an action that is carried out by a group of agents in coordination.**Joint Attention**: The shared focus of two individuals on an object. It is achieved when one individual alerts another to an object by means of eye-gazing, pointing, or other verbal or non-verbal indications.**Joint Training**: Training multiple models or multiple parts of a model simultaneously.**Joule**: A derived unit of energy in the International System of Units. It is also used to measure the computational energy efficiency in AI hardware.**JPEG (Joint Photographic Experts Group)**: A commonly used method of lossy compression for digital images, particularly for those images produced by digital photography.**JSX (JavaScript XML)**: A syntax extension for JavaScript that is typically used with React to describe what the UI should look like.**JVM Profiling**: The process of monitoring various aspects of the Java Virtual Machine to identify bottlenecks or performance issues.**JVM Tuning**: The process of adjusting the JVM settings to optimize performance for specific applications or tasks.**Jaccard Similarity Coefficient**: A statistic used in understanding the similarities between sample sets. It is defined as the size of the intersection divided by the size of the union of the sample sets.

## K

**k-Nearest Neighbors (k-NN)**: A simple, non-parametric algorithm used for classification and regression by comparing the distance of a new data point to the k closest labeled data points.**K-Means Clustering**: An unsupervised learning algorithm that groups data into k number of clusters based on feature similarity.**K-Fold Cross-Validation**: A resampling procedure used to evaluate machine learning models on a limited data sample.**K-Medoids**: Similar to k-means, this clustering algorithm is more robust to noise and outliers by using medoids instead of means for clustering.**K-Means++**: An algorithm for choosing the initial values (or “seeds”) for the k-means clustering algorithm.**K-Mer**: A substring of length k; it is a concept used in bioinformatics for algorithms that analyze sequences.**K-Bandit Problems**: A problem in reinforcement learning where an agent must choose between k different options with uncertain rewards.**K-D Tree (k-dimensional tree)**: A space-partitioning data structure for organizing points in a k-dimensional space.**K-L Divergence (Kullback-Leibler Divergence)**: A measure of how one probability distribution diverges from a second, expected probability distribution.**K-S Test (Kolmogorov-Smirnov Test)**: A nonparametric test of the equality of continuous, one-dimensional probability distributions.**Kanerva Model**: A sparse distributed memory model inspired by the way the human brain processes information.**Kappa Statistic**: A statistical measure of inter-rater agreement for categorical items.**Karhunen-Loève Transform**: A linear orthogonal transformation that converts a set of possibly correlated variables into a set of values of linearly uncorrelated variables.**Kernel**: In machine learning, a function used in support vector machines to enable them to work in a higher-dimensional space.**Kernel Density Estimation**: A non-parametric way to estimate the probability density function of a random variable.**Kernel Method**: A class of algorithms for pattern analysis, whose best known member is the support vector machine.**Kernel Trick**: A method used in machine learning to implicitly map input data into a higher-dimensional feature space.**Key-Value Memory Networks**: A type of memory-augmented neural network that uses an associative array abstraction to store and retrieve information.**Knowledge Base**: In AI, a technology used to store complex structured and unstructured information used by a computer system.**Knowledge Engineering**: The field of AI that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.**Knowledge Graph**: A knowledge base that uses a graph-structured data model or topology to integrate data.**Knowledge Representation**: The field of AI dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks.**Kohonen Map (Self-Organizing Map)**: A type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional representation of the input space.**Kolmogorov Complexity**: A measure of the computational resources needed to specify a dataset.**Kolmogorov-Arnold Networks (KANs)**: A new approach to neural networks inspired by the Kolmogorov-Arnold representation theorem, offering a promising alternative to Multi-Layer Perceptrons (MLPs) for complex function approximations.**Krylov Subspace**: A sequence of vector spaces used in numerical linear algebra for solving linear equations and eigenvalue problems.**Kurtosis**: A measure of the “tailedness” of the probability distribution of a real-valued random variable.**Knowledge Discovery**: The process of discovering useful knowledge from a collection of data.**Knowledge Extraction**: The creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources.**Knowledge-Based System**: A system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action.**Knowledge Ontology**: In AI, an ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.

## L

**Labeled Data**: Data that has been tagged with one or more labels identifying certain properties or classifications.**Lagrange Multiplier**: A strategy for finding the local maxima and minima of a function subject to equality constraints.**Lambda Architecture**: A data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream processing methods.**Lanczos Algorithm**: An iterative algorithm used to estimate the eigenvalues and eigenvectors of a large sparse matrix.**Language Model**: A statistical model that determines the probability of a sequence of words.**Latent Dirichlet Allocation (LDA)**: A generative statistical model that explains sets of observations through unobserved groups that explain why some parts of the data are similar.**Latent Semantic Analysis (LSA)**: A technique in natural language processing for analyzing relationships between a set of documents and the terms they contain.**Latent Variable**: A variable that is not directly observed but is inferred from other variables that are observed and directly measured.**Layer**: In neural networks, a collection of neurons that operate in parallel and are connected to other layers.**Leaky ReLU**: A type of activation function used in neural networks that allows a small, non-zero gradient when the unit is not active.**Learning Rate**: A hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.**Least Squares**: A method for estimating the unknown parameters in a linear regression model by minimizing the sum of the squares of the differences between the observed and predicted values.**Levenshtein Distance**: A string metric for measuring the difference between two sequences.**Lexical Analysis**: The process of converting a sequence of characters into a sequence of tokens.**Lift**: In association rule learning, a measure of how much more often the antecedent and consequent of a rule occur together than expected if they were statistically independent.**Linear Discriminant Analysis (LDA)**: A method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.**Linear Regression**: A linear approach to modeling the relationship between a scalar response and one or more explanatory variables.**Link Analysis**: A data-analysis technique used to evaluate relationships between nodes.**Linguistic Variable**: A variable whose values are words or sentences in natural or artificial language.**Lipschitz Continuity**: A condition on a function to have bounded variation, used in mathematical optimization.**Local Minima**: In mathematical optimization, a point where the function value is lower than at nearby points, but possibly higher than at a distant point.**Local Search**: An optimization technique that starts with an initial solution and iteratively moves to a neighbor solution with a better objective function value.**Log-Likelihood**: A logarithm of the likelihood function used in statistical models.**Logistic Regression**: A statistical model that uses a logistic function to model a binary dependent variable.**Long Short-Term Memory (LSTM)**: A type of recurrent neural network capable of learning long-term dependencies.**Loss Function**: A function that maps values of one or more variables onto a real number intuitively representing some “cost” associated with the event.**Lower Bound**: In mathematical optimization, the lowest possible value of an objective function within its domain.**LSTM Unit**: A building block for layers of a recurrent neural network (RNN) which allows RNNs to remember inputs over a long period of time.**Ludic Fallacy**: The misuse of games to model real-life situations.**Luhn Algorithm**: An algorithm used to validate a variety of identification numbers.

## M

**Machine Learning**: The study of computer algorithms that improve automatically through experience and by the use of data.**Macro**: In programming, a rule or pattern that specifies how a certain input sequence should be mapped to a replacement output sequence.**Manifold Learning**: A type of unsupervised learning that seeks to describe datasets as low-dimensional manifolds embedded in high-dimensional spaces.**Margin**: In classification, the distance between the decision boundary and the closest data points.**Markov Chain**: A stochastic model describing a sequence of possible events where the probability of each event depends only on the state attained in the previous event.**Markov Decision Process (MDP)**: A mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.**Mask R-CNN**: A state-of-the-art deep learning algorithm used for object instance segmentation.**Mean Absolute Error (MAE)**: A measure of errors between paired observations expressing the same phenomenon.**Mean Squared Error (MSE)**: The average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.**Meta-Learning**: A subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments.**Metric Learning**: A type of learning where the goal is to learn a distance function that measures how similar or related two objects are.**Minimax**: An algorithm used in decision making and game theory to minimize the possible loss for a worst case (maximum loss) scenario.**Minimum Viable Product (MVP)**: A product with just enough features to satisfy early customers and provide feedback for future product development.**Mixed Reality (MR)**: The merging of real and virtual worlds to produce new environments and visualizations where physical and digital objects co-exist and interact in real time.**Model**: In machine learning, an abstract representation of a process or a system that is trained to make predictions or decisions based on data.**Model Deployment**: The process of integrating a machine learning model into an existing production environment to make practical business decisions based on data.**Model Evaluation**: The process of using different metrics to assess the performance of a machine learning model.**Model Selection**: The task of selecting a statistical model from a set of candidate models, given data.**Model Tuning**: The process of adjusting the parameters of a machine learning model to improve its performance.**Modularity**: The degree to which a system’s components may be separated and recombined, often used in the context of managing complexity in neural networks.**Monte Carlo Method**: A broad class of computational algorithms that rely on repeated random sampling to obtain numerical results.**Monte Carlo Tree Search (MCTS)**: A heuristic search algorithm for some kinds of decision processes, most notably those employed in game play.**Multi-Agent System**: A system composed of multiple interacting intelligent agents within an environment.**Multi-Class Classification**: A classification task with more than two classes; each sample is assigned to one and only one label.**Multi-Label Classification**: A classification task where each sample is mapped to a set of target labels (not just one).**Multi-Layer Perceptron (MLP)**: A class of feedforward artificial neural network that consists of at least three layers of nodes.**Multi-Objective Optimization**: An area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.**Multimodal Learning**: Machine learning models that process and relate information from multiple different modalities, such as a system that analyzes both images and text.**Mutual Information**: A measure of the mutual dependence between two variables.**Mixture of Experts**: A machine learning ensemble technique where individual models (the “experts”) specialize in different parts of the input space.

## N

**Naive Bayes**: A family of simple probabilistic classifiers based on applying Bayes’ theorem with strong independence assumptions between the features.**Natural Language Generation (NLG)**: The process of producing meaningful phrases and sentences in the form of natural language from some internal representation.**Natural Language Processing (NLP)**: A field of AI that gives the machines the ability to read, understand, and derive meaning from human languages.**Natural Language Understanding (NLU)**: A subfield of NLP involved in the interactions between computers and human (natural) languages, specifically how to program computers to process and analyze large amounts of natural language data.**Nearest Neighbor Algorithm**: An algorithm that classifies each data point based on how its neighbors are classified.**Negative Sampling**: A technique used to reduce the computation time in training large neural networks by randomly sampling a small subset of the negative examples.**Neural Architecture Search (NAS)**: The process of automating the design of artificial neural networks.**Neural Network**: A network or circuit of neurons, or in a modern sense, an artificial neural network composed of artificial neurons or nodes.**Neuroevolution**: A form of machine learning that uses evolutionary algorithms to train artificial neural networks.**Neuro-Fuzzy**: A hybrid intelligent system that synergizes neural networks and fuzzy logic principles.**Neuroinformatics**: An interdisciplinary field that encompasses the organization of neuroscience data and application of computational models and analytical tools.**Neuromorphic Engineering**: The use of systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.**Newton’s Method**: An optimization algorithm that finds the minimum (or maximum) of a function by iteratively moving towards the stationary point where the function’s derivative is zero.**N-Gram**: A contiguous sequence of n items from a given sample of text or speech.**NLP Toolkit (NLTK)**: A suite of libraries and programs for symbolic and statistical natural language processing for English.**Node**: In the context of neural networks, a node is a single processing element of a neural network.**Noise**: In the context of machine learning, noise refers to irrelevant or meaningless data points that can negatively impact the performance of a model.**Non-Linear Regression**: A form of regression analysis in which observational data is modeled by a function that is a nonlinear combination of the model parameters and depends on one or more independent variables.**Non-Parametric Model**: A model that does not assume a particular form for the relationship between predictors and the target variable.**Normalization**: The process of scaling individual samples to have unit norm in machine learning.**Normative Agent**: An agent that acts based on a set of rules or guidelines.**Not-Exclusive-Nor (NEN)**: A logical gate that is true when both inputs are different.**Novelty Detection**: The identification of new or unknown data or signals that a machine learning system is not aware of during training.**NoSQL Database**: A database that provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.**N-Tuple Network**: A pattern recognition model consisting of a set of n-tuples that can be used for playing board games.**Null Hypothesis**: In statistical hypothesis testing, the hypothesis that there is no effect or no difference.**Numerical Optimization**: The selection of a best element, with regard to some criterion, from some set of available alternatives in a numerical fashion.**Numpy**: A library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.**Nyquist Rate**: The minimum rate at which a signal can be sampled without introducing errors, which is twice the highest frequency present in the signal.**Nash Equilibrium**: A solution concept of a non-cooperative game involving two or more players, where no player has anything to gain by changing only their own strategy.

## O

**Object Recognition**: The ability of computer vision systems to identify and classify various objects within an image or video.**Objective Function**: A function used during the training of a machine learning model that the algorithm seeks to minimize or maximize.**Occlusion**: In computer vision, this refers to the blockage or obstruction of a view.**OCR (Optical Character Recognition)**: The electronic conversion of images of typed, handwritten, or printed text into machine-encoded text.**Off-Policy Learning**: A type of reinforcement learning where the policy being learned about is different from the policy used to generate the data.**On-Policy Learning**: A type of reinforcement learning where the policy being learned is the same as the policy used to generate the data.**One-Hot Encoding**: A process of converting categorical variables into a form that could be provided to machine learning algorithms to do a better job in prediction.**One-Shot Learning**: A machine learning technique where the model learns from only a single training example per class.**Online Learning**: A model training methodology where the model is updated continuously as new data arrives.**Ontology**: In the context of AI, it refers to an explicit specification of a conceptualization.**Open Domain Question Answering**: A system that provides answers to questions posed in natural language on any topic.**Open Set Recognition**: The ability of models to recognize classes that were not seen during training.**Open Source AI**: AI software where the source code is available to the public and can be modified and shared.**OpenAI**: An AI research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc.**OpenCV**: An open-source computer vision and machine learning software library.**Operant Conditioning**: A method of learning that employs rewards and punishments for behavior in psychology, which can be applied in AI for reinforcement learning.**Operator**: In AI, it refers to a function that maps from one state space to another in the context of problem-solving.**Optical Flow**: The pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and the scene.**Optimization**: The process of making a system, design, or decision as effective or functional as possible in machine learning and AI.**Optimization Algorithm**: An algorithm used in machine learning to adjust the parameters of a model to minimize a loss function.**Oracle**: In AI, an oracle is a system that provides the correct answers or solutions, often used in theoretical contexts.**Ordinal Regression**: A type of regression analysis used for predicting an ordinal variable.**Overfitting**: A modeling error in machine learning which occurs when a function is too closely fit to a limited set of data points.**Overhead**: In computing, it refers to the extra processing or communication time taken by computational tasks.**Oversampling**: A technique used to adjust the class distribution of a data set (i.e., the ratio between different classes).

## P

**PAC Learning**: Probably Approximately Correct Learning, a framework for mathematical analysis of machine learning.**Parallel Processing**: The simultaneous processing of the same task on multiple processors to increase computing efficiency.**Parameter Tuning**: The process of adjusting the parameters of a machine learning model to improve its performance.**Pattern Recognition**: The identification of patterns and regularities in data using machine learning algorithms.**Perceptron**: A type of artificial neuron used in supervised learning.**Performance Metric**: A measure used to assess the performance of a machine learning model.**Personalization**: Tailoring content or experiences to individual users based on their preferences and behavior.**Phenetics**: The classification of organisms based on their observable characteristics, often using machine learning techniques.**Philosophy of AI**: The study of the fundamental nature, ethics, and implications of artificial intelligence.**Photogrammetry**: The use of photography in surveying and mapping to measure distances between objects.**Physical Symbol System**: A system that produces intelligent action by manipulating symbols and combining them into structures.**Pipelining**: A technique in computing where multiple processing stages are performed in a sequence.**Planning Algorithm**: An algorithm that formulates a sequence of actions to achieve a specific goal.**Point Cloud**: A set of data points in space, often used in 3D modeling and computer vision.**Policy Gradient Methods**: A class of reinforcement learning algorithms that optimize policy directly.**Polynomial Regression**: A type of regression analysis that models the relationship between the independent variable and the dependent variable as an nth degree polynomial.**Pooling**: A technique used in convolutional neural networks to reduce the spatial size of the representation.**Population Coding**: A method in neuroscience and AI that represents information across a population of neurons.**Positive Reinforcement**: In machine learning, the increase of the strength of behavior due to the addition of a reward following the behavior.**Predictive Analytics**: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.**Predictive Modeling**: The process of creating, testing, and validating a model to best predict the probability of an outcome.**Prescriptive Analytics**: The area of business analytics dedicated to finding the best course of action for a given situation.**Principal Component Analysis (PCA)**: A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.**Probabilistic Graphical Models**: A framework for modeling complex multivariate distributions to gain insights about the world and make predictions.**Program Synthesis**: The process of automatically constructing a program that satisfies a given high-level specification

## Q

**Quantum Computing**: A type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data.**Q-Learning**: A form of model-free reinforcement learning that learns the value of an action in a particular state.**Qualitative Reasoning**: Reasoning that deals with the quality rather than the quantity, often used in AI to handle scenarios with incomplete knowledge.**Quality Control**: The use of AI to monitor and improve the quality of products and services.**Quantification**: The process of turning qualitative measures into quantitative metrics.**Quantitative Analysis**: The use of mathematical and statistical methods in AI to model and understand data.**Query**: A request for information from a database, which in AI can be processed by natural language processing systems.**Question Answering**: An AI system designed to answer questions posed by humans in a natural language.**Quick, Draw!**: A game developed by Google that uses neural network AI to recognize doodles.**Quiescent Search**: In game playing, a search algorithm that looks for a state where there is no immediate threat or capture, to avoid the horizon effect.**Quintuple**: In automata theory, a five-tuple that represents a finite state machine in formal language theory.**Quorum Sensing**: In bio-inspired AI, the ability of distributed systems to work together based on population density.**Quadratic Discriminant Analysis**: A statistical method used in machine learning to separate measurements of two or more classes of objects or events by a quadratic surface.**Qualia**: In philosophy of mind, individual instances of subjective, conscious experience which can be considered in AI ethics and consciousness studies.**Quantum Machine Learning**: An emerging interdisciplinary research area at the intersection of quantum physics and machine learning.**Quantum Neural Network**: A neural network model that is based on the principles of quantum mechanics.**Quasi-Newton Methods**: Optimization algorithms used to find local maxima and minima of functions.**Quasilinear Model**: A model in which the relationship between variables is linear in the parameters, used in certain AI applications.**Queue**: In AI, particularly in search algorithms, a data structure that holds a list of elements that are to be processed in some order.**Quicksort**: A sorting algorithm, which, though not directly an AI term, is often used in AI for sorting data efficiently.**Quintic Function**: A function of degree five, which in AI can be used for modeling and curve fitting.**Quipu**: An ancient Inca device for recording information, mentioned in discussions about the history of computing and AI.**Quadratic Unconstrained Binary Optimization (QUBO)**: A mathematical formulation used in quantum computing and optimization problems in AI.**Quadratic Programming**: A type of optimization problem in which a quadratic function is optimized over linear constraints.**Quotient Space Theory**: A theory used in robotics and AI for simplifying complex problems by dividing them into smaller, more manageable sub-problems.

## R

**R-CNN (Region-based Convolutional Neural Networks)**: A type of deep neural network designed to solve object detection tasks.**Radial Basis Function (RBF)**: A function used in various types of neural networks, often as a kernel in support vector machines.**Random Forest**: An ensemble learning method for classification, regression, and other tasks that operates by constructing a multitude of decision trees.**Ranking**: The task of generating a ranked list of items based on certain criteria.**RapidMiner**: A data science software platform that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.**Rational Agent**: An agent that acts to achieve the best outcome or, when there is uncertainty, the best expected outcome.**Ray Tracing**: A rendering technique for generating an image by tracing the path of light as pixels in an image plane.**Reactive AI**: AI systems that perceive their environment and react to it without possessing an internal model of the world.**Real-Time AI**: AI systems that provide immediate responses in dynamic environments, often within milliseconds.**Reasoning System**: A system that generates conclusions from available knowledge using logical techniques.**Recommender Systems**: Systems that predict the ‘rating’ or ‘preference’ a user would give to an item.**Recurrent Neural Network (RNN)**: A class of neural networks where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior.**Reinforcement Learning**: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.**Relational AI**: AI that can understand and reason about relationships between entities.**Relational Database**: A database structured to recognize relations among stored items of information.**Reliability**: The degree to which the outcome of a system is consistent over multiple trials.**Remote Sensing**: The process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance.**Representation Learning**: A set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.**Residual Networks (ResNets)**: A type of convolutional neural network architecture that introduces shortcuts to jump over some layers.**Restricted Boltzmann Machine (RBM)**: A network of symmetrically connected, neuron-like units that make stochastic decisions about whether to be on or off.**Retrieval-Based Models**: Models that retrieve information from a dataset rather than generating new information or patterns.**Reversible Computing**: A model of computing where the computational process to some extent is reversible.**Reward Function**: A function that maps a state of the world and an action onto a reward signal.**Robotics**: The branch of technology that deals with the design, construction, operation, and application of robots.**Robustness**: The ability of an AI system to cope with errors during execution and with erroneous input.**Rule-Based System**: A system that uses rules as the knowledge representation instead of procedural code.**Rule Learning**: A method of machine learning that focuses on learning rule sets from data.**Runtime**: The period during which a computer program is executing.**Rust**: A multi-paradigm programming language designed for performance and safety, particularly safe concurrency.**RNN (Recurrent Neural Networks)**: A type of neural network where connections between units form a directed cycle, allowing it to use internal state

## S

**Sample**: A subset of data or a statistical population used for analysis and modeling.**Sampling**: The process of selecting a subset of individuals from a statistical population to estimate characteristics of the whole population.**SARSA (State-Action-Reward-State-Action)**: An algorithm in reinforcement learning that uses the Q-learning method with a slight variation.**Scalability**: The capability of a system to handle a growing amount of work or its potential to be enlarged to accommodate that growth.**Scikit-learn**: An open-source machine learning library for Python.**Search Algorithm**: An algorithm for finding an item with specified properties within a collection of items.**Semi-Supervised Learning**: A class of machine learning tasks and techniques that also make use of unlabeled data for training.**Sentiment Analysis**: The process of computationally determining whether a piece of writing is positive, negative, or neutral.**Sequence Learning**: A type of learning where the model is trained to recognize sequences, such as time series or text.**Sequential Decision Making**: The process of making decisions over time by considering the current state and the sequence of states that led to it.**Serendipity**: The occurrence and development of events by chance in a happy or beneficial way, which can be an aspect of AI in discovering unexpected patterns.**Serverless Computing**: A cloud-computing execution model where the cloud provider runs the server and dynamically manages the allocation of machine resources.**Shallow Learning**: Machine learning methods that do not use deep neural networks and typically involve fewer layers of processing or transformations.**Signal Processing**: The analysis, interpretation, and manipulation of signals. Signals are typically electrical or optical representations of time-varying or spatial-varying physical quantities.**Similarity Measure**: A metric used to determine how similar two data objects are.**Simulated Annealing**: A probabilistic technique for approximating the global optimum of a given function.**Simulation**: The imitation of the operation of a real-world process or system over time.**Single-Layer Perceptron**: The simplest type of artificial neural network, consisting of only one layer of nodes.**SLAM (Simultaneous Localization and Mapping)**: A technique used by robots and autonomous vehicles to build up a map within an unknown environment while keeping track of their current position.**Smart Agent**: An agent that can learn from its environment and experiences to perform tasks in a more efficient way.**Social Network Analysis**: The process of investigating social structures through the use of networks and graph theory.**Soft Computing**: A computing approach that deals with approximate models and gives solutions to complex real-life problems.**Software Agent**: A software program that acts for a user or other program in a relationship of agency.**Spiking Neural Networks**: A type of artificial neural network model that more closely resembles biological neural networks.**Statistical Learning Theory**: A framework for machine learning drawing from the fields of statistics and functional analysis.**Stochastic Gradient Descent**: An iterative method for optimizing an objective function with suitable smoothness properties.**Strong AI**: AI with the ability to apply intelligence to any problem, rather than just specific problems, akin to human cognitive abilities.**Structured Data**: Data that adheres to a pre-defined data model and is therefore straightforward to analyze.**Sub-symbolic AI**: AI methods that are not based on high-level “symbolic” reasoning; they operate at a lower level, closer to the raw data.**Supervised Learning**: A type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions.

## T

**Tabu Search**: An optimization algorithm that uses local search methods and a tabu list to avoid cycles.**TensorFlow**: An open-source software library for dataflow and differentiable programming across a range of tasks.**Text Mining**: The process of deriving high-quality information from text using computational linguistics and pattern recognition.**Text-to-Speech (TTS)**: A form of speech synthesis that converts text into spoken voice output.**Theory of Mind**: The ability to attribute mental states — beliefs, intents, desires, emotions, knowledge — to oneself and others.**Thompson Sampling**: An algorithm for choosing actions that address the exploration-exploitation dilemma in multi-armed bandit problems.**Time Series Analysis**: Methods that analyze time series data to extract meaningful statistics and other characteristics.**Tokenization**: The process of converting a sequence of characters into a sequence of tokens.**Topological Data Analysis**: A method of applying topology and the properties of geometric shapes to data.**Transfer Learning**: The reuse of a pre-trained model on a new problem, adapting it to a related domain.**Transformer Models**: A type of deep learning model that uses self-attention mechanisms to process sequences of data.**Tree Search**: A search algorithm that traverses the structure of a tree to find specific values or paths.**Triplet Loss**: A loss function used to learn embeddings in which an anchor is compared to a positive and a negative example.**Truncated SVD (Singular Value Decomposition)**: A matrix factorization technique that generalizes the eigendecomposition of a square normal matrix.**Tsetlin Machine**: A type of learning machine that uses a collective system of learning automata to solve problems.**T-SNE (t-Distributed Stochastic Neighbor Embedding)**: A machine learning algorithm for visualization developed by Laurens van der Maaten and Geoffrey Hinton.**Turing Test**: A test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.**Turk’s Head**: In AI, this refers to a type of algorithmic puzzle or challenge.**Tuple**: A finite ordered list of elements, particularly relevant in the context of relational databases.**Tweak**: In AI, making small adjustments to algorithms or models to improve performance.**Type I Error**: The incorrect rejection of a true null hypothesis (also known as a “false positive”).**Type II Error**: The failure to reject a false null hypothesis (also known as a “false negative”).**Typicality**: The degree to which a particular case is typical for its kind, often used in case-based reasoning in AI.**Typology**: The study and interpretation of types and symbols, originally in psychology, and now also in AI.**Tensor**: A mathematical object analogous to but more general than a vector, represented as an array of components.**Temporal Difference Learning**: A prediction method that learns by bootstrapping from the current estimate of the value function.**Terabyte**: A unit of information equal to one trillion bytes, often used to measure data sets in AI.**Terminator Algorithm**: A hypothetical algorithm that could bring about the end of humanity, often discussed in the context of AI safety.**Test Set**: A set of data used to assess the strength and utility of a predictive model.**Text Analytics**: The process of converting unstructured text data into meaningful data for analysis.

## U

**Ubiquitous Computing**: Computing that is made to appear everywhere and anywhere using any device, in any location, and in any format.**Uncertainty**: In AI, the lack of certainty, a state of having limited knowledge where it is impossible to exactly describe the existing state or future outcome.**Unsupervised Learning**: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.**Utility Function**: A function in AI that maps a state (or a sequence of states) of the world into a measure of utility, or value.**Universal Approximation Theorem**: A theory that states a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of (\mathbb{R}^n), under mild assumptions on the activation function.**User Experience (UX)**: In AI, the overall experience of a person using a product such as a website or a computer application, especially in terms of how easy or pleasing it is to use.**User Interface (UI)**: The means by which the user and a computer system interact, in particular the use of input devices and software.**User Modeling**: The process of building up and updating a model of a user in AI systems.**Utility-Based Agent**: An AI agent that tries to maximize its own utility, or happiness, based on a utility function.**Unstructured Data**: Data that does not have a pre-defined data model or is not organized in a pre-defined manner, such as texts, images, and social media posts**Uniform Cost Search**: A search algorithm used in AI that expands the least costly node first, ensuring that the search is both complete and optimal. It’s particularly useful when all actions have the same cost. When costs vary, it finds the path with the lowest total cost. This method is also known as the least-cost search.**Unsupervised Learning**: A type of machine learning algorithm where the model learns from unlabeled data without explicit supervision. It identifies patterns and structures in the data without predefined output labels.**Utility Function**: A function that maps a state (or a sequence of states) of the world into a measure of utility or value. It helps AI agents make decisions by evaluating the desirability of different outcomes.**Universal Approximation Theorem**: A mathematical result that states that a feed-forward neural network with a single hidden layer containing a finite number of neurons can approximate any continuous function on compact subsets of (\mathbb{R}^n), given appropriate activation functions.**Unstructured Data**: Data that lacks a predefined data model or organization, such as text, images, audio, or video. Unstructured data presents challenges for analysis but is essential for many AI applications.**User Experience (UX)**: The overall experience of a person using a product, system, or service, especially in terms of how easy or pleasing it is to use. UX design plays a crucial role in AI applications to ensure user satisfaction.**User Interface (UI)**: The means by which users interact with software, hardware, or other digital systems. UI design focuses on creating intuitive and efficient interfaces for users to interact with AI applications.**Utility-Based Agent**: An AI agent that makes decisions based on maximizing its own utility or happiness, guided by a utility function. Utility-based agents weigh different actions to achieve the best outcome.**Univariate Analysis**: The examination of a single variable or feature in a dataset, often used to understand its distribution, central tendency, and variability.

## V

**Validation**: The process of evaluating the performance of a model using a separate dataset that was not used during training.**Value Function**: In reinforcement learning, a function that estimates the expected return of a state or state-action pair.**Variable**: An element, feature, or factor that is liable to vary or change.**Variational Autoencoder (VAE)**: A type of autoencoder that generates new data instances that are similar to the input data.**Vector**: A quantity or phenomenon that has two independent properties: magnitude and direction.**Vector Space Model**: A mathematical model for representing text documents as vectors of identifiers.**Velocity**: In big data, the speed at which the data is created, stored, analyzed, and visualized.**Veracity**: In big data, the quality or trustworthiness of the data.**Version Control**: A system that records changes to a file or set of files over time so that specific versions can be recalled later.**Virtual Agent**: A computer-generated, animated, artificial intelligence virtual character that serves as an online customer service representative.**Virtual Reality (VR)**: A simulated experience that can be similar to or completely different from the real world.**Vision Processing Unit (VPU)**: A type of microprocessor designed to accelerate machine vision tasks.**Visual Analytics**: The science of analytical reasoning supported by interactive visual interfaces.**Visual Recognition**: The ability of software to identify objects, places, people, writing, and actions in images.**Voice Recognition**: The ability of a machine or program to receive and interpret dictation or understand and carry out spoken commands.**Volatility**: In big data, the frequency of data updates and how long data is valid for use.**Volume**: In big data, the amount of data generated and stored.**Voronoi Diagram**: A partitioning of a plane into regions based on distance to points in a specific subset of the plane.**Voting Ensemble**: A machine learning model that combines the predictions from multiple other models.**Vulnerability**: A weakness in a system that can be exploited by threats to gain unauthorized access to an asset.**VGGNet**: A deep convolutional neural network architecture known for its simplicity and deep layers, used widely in image recognition tasks.**Virtual Assistant**: An AI-powered software agent that can perform tasks or services for an individual based on commands or questions.**Virtual Environment**: In machine learning, a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages.**Virtual Machine (VM)**: A software computer that, like a physical computer, runs an operating system and applications.**Virtual Memory**: A memory management capability of an operating system that uses hardware and software to allow a computer to compensate for physical memory shortages.**Virtual Network**: A software-defined network that exists within a single physical network or spans multiple physical networks.**Virtualization**: The creation of a virtual version of something, such as a server, a desktop, a storage device, network resources, or an operating system.**Vision System**: An integrated system for processing visual data; it can include everything from image capturing to processing and analysis.**Visual Question Answering (VQA)**: A research area in AI where systems try to answer questions posed in natural language about visual content.**Visual Search**: The ability of AI to analyze a visual image as the stimulus for conducting a search query.

## W

**Wake Word**: A specific word or phrase that activates voice-controlled devices and virtual assistants.**Wald’s Sequential Analysis**: A statistical test that allows for sequential testing of hypotheses.**WAN (Wide Area Network)**: A telecommunications network that extends over a large geographic area for the purpose of computer networking.**Wasserstein Distance**: A measure used in statistics to quantify the distance between two probability distributions.**Weak AI**: AI systems designed to handle one particular task or a set of related tasks, also known as narrow AI.**Web Crawling**: The process by which a program or automated script browses the World Wide Web in a methodical, automated manner.**Web Mining**: The process of using data mining techniques to extract information from web content.**Weight**: In neural networks, a parameter that is adjusted during training to minimize the loss function.**Weight Decay**: A regularization technique that adds a penalty to the loss function based on the magnitude of the weights of the neural network.**Weight Initialization**: The process of setting the initial values of the weights of a neural network before training begins.**Weighted Graph**: A graph in which each edge is assigned a weight or cost, often used in pathfinding algorithms.**Weighted Majority Algorithm**: An algorithm that combines the predictions of several algorithms to make a final prediction.**Whitelist**: A list of entities that are granted a particular privilege, service, mobility, access, or recognition.**Wide Learning**: A machine learning approach that creates a wide linear model to capture a large number of sparse feature interactions.**Word Embedding**: A learned representation for text where words that have the same meaning have a similar representation.**WordNet**: A large lexical database of English, used in computational linguistics and natural language processing.**Workflow Automation**: The design, execution, and automation of processes based on workflow rules where human tasks, data, or files are routed between people or systems.**Wrapper Method**: A feature selection technique in machine learning where different subsets of features are used to train a model, and the best performing subset is selected.**Write-Through Cache**: A caching technique where every write to the cache causes a write to main memory.

## X

**XAI (Explainable AI)**: Refers to artificial intelligence and machine learning techniques that can be understood by humans and are transparent about how they make decisions or take actions.**XGBoost**: A scalable and accurate implementation of gradient boosting machines, which is a machine learning algorithm used for regression and classification problems.**XML (eXtensible Markup Language)**: A markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. It’s often used in the context of AI for data representation and exchange.**XPath**: A language for selecting nodes from an XML document, which can be used in AI for parsing and analyzing structured data.**XQuery**: A query and functional programming language that is designed to query collections of XML data. It’s used in AI for data retrieval and analysis.**X-Ray Vision**: In AI, this term can refer to the ability of computer vision systems to interpret images in a way that seems to see through solid objects, typically used in medical imaging and security.**Xenobot**: A term used to describe a new type of bio-robotic organism that is designed using AI algorithms and living cells.**XenonPy**: A Python library for materials informatics with machine learning and artificial intelligence

## Y

**Yann LeCun**: A computer scientist who is well-known for his work in deep learning and artificial neural networks.**YOLO (You Only Look Once)**: A real-time object detection system that applies a single neural network to the full image, dividing the image into regions and predicting bounding boxes and probabilities for each region.**Yottabyte**: A unit of information or computer storage equal to one septillion bytes. As AI and big data continue to evolve, the term represents the vast amount of data that can be processed and analyzed.**YouTube-8M**: A large-scale labeled video dataset that consists of millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities.**Yield**: In the context of AI, it often refers to the success rate of an AI system in correctly performing its tasks.**Y-axis**: In machine learning and data visualization, the vertical axis of a chart or graph.**YARN (Yet Another Resource Negotiator)**: A cluster management technology used for cluster scheduling and resource management of big data applications.**YASK (Yet Another Stencil Kernel)**: A framework designed to facilitate high-performance stencil code optimization for various architectures.

## Z

**Zero-Shot Learning**: A machine learning technique where the model is designed to correctly handle tasks it has not explicitly seen during training.**Zettabyte**: A unit of digital information storage that equals one sextillion bytes, often used to quantify the massive amount of data processed by AI systems.**Z-Test**: A statistical test used to determine whether two population means are different when the variances are known and the sample size is large.**Zigbee**: A specification for a suite of high-level communication protocols using low-power digital radios, which can be used in AI for Internet of Things (IoT) applications.**Z-Score**: A numerical measurement that describes a value’s relationship to the mean of a group of values, used in AI for normalization of data.**ZSL (Zero-Shot Learning)**: An abbreviation for zero-shot learning, emphasizing the ability of models to generalize to new tasks without prior examples.**Zookeeper**: An open-source server which enables highly reliable distributed coordination, used in distributed AI systems for maintaining configuration information, naming, and providing distributed synchronization.**Zooniverse**: A platform for people-powered research that utilizes the power of volunteers to assist with scientific research that machines cannot do alone, often involving AI and machine learning tasks.**Z-transform**: A mathematical transform used in signal processing and control theory, which can be applied in AI for analyzing discrete signals.**Z-Wave**: A wireless communications protocol used primarily for home automation, which can be integrated with AI systems for smart home solutions